





import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_a     :    Optional[int]         =						random.Random()

if is_torch_available():
			import torch
def 							a_	(    __magic_name__		,     __magic_name__=1.0		,     __magic_name__=None		,     __magic_name__=None   )				->			Tuple:




				"""simple docstring"""






				if rng is None:
								snake_case   :					Tuple    						=				global_rng

				snake_case   :					Optional[int]    						=				[]
				for batch_idx in range(shape[0]   ):
								values.append([]   )
								for _ in range(shape[1]   ):
												values[-1].append(rng.random() * scale   )

				return values
class   a_ (			unittest.TestCase      ):

			def __init__(   self   :  Union[str, Any]     , UpperCAmelCase__   :  Dict     , UpperCAmelCase__   :  Optional[Any]=7     , UpperCAmelCase__   :  str=400     , UpperCAmelCase__   :  Tuple=2_000     , UpperCAmelCase__   :  str=1     , UpperCAmelCase__   :  Union[str, Any]=0.0     , UpperCAmelCase__   :  List[str]=16_000     , UpperCAmelCase__   :  Any=True     , UpperCAmelCase__   :  Optional[int]=True     , ):






							"""simple docstring"""
							snake_case   :					int    						=				parent
							snake_case   :					Optional[int]    						=				batch_size
							snake_case   :					Optional[int]    						=				min_seq_length
							snake_case   :					Optional[int]    						=				max_seq_length
							snake_case   :					Optional[Any]    						=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							snake_case   :					Optional[int]    						=				feature_size
							snake_case   :					int    						=				padding_value
							snake_case   :					Dict    						=				sampling_rate
							snake_case   :					List[str]    						=				return_attention_mask
							snake_case   :					Union[str, Any]    						=				do_normalize

			def      lowerCAmelCase(   self   :  Optional[Any]						):






							"""simple docstring"""
							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							    "do_normalize": self.do_normalize,
							}



			def      lowerCAmelCase(   self   :  str     , UpperCAmelCase__   :  Optional[int]=False     , UpperCAmelCase__   :  str=False						):






							"""simple docstring"""


							def _flatten(UpperCAmelCase__   :  Optional[Any]						):
											return list(itertools.chain(*UpperCAmelCase__						)						)

							if equal_length:
											snake_case   :					Optional[int]    						=				floats_list((self.batch_size, self.max_seq_length)						)
							else:
											# make sure that inputs increase in size
											snake_case   :					Tuple    						=				[
											    _flatten(floats_list((x, self.feature_size)						)						)
											    for x in range(self.min_seq_length     , self.max_seq_length     , self.seq_length_diff						)
											]

							if numpify:
											snake_case   :					List[str]    						=				[np.asarray(UpperCAmelCase__						) for x in speech_inputs]

							return speech_inputs
@require_torch
@require_torchaudio
class   a_ (			a		,							unittest.TestCase      ):
			A__      :			Union[str, Any]       	=  ASTFeatureExtractor

			def      lowerCAmelCase(   self   :  List[str]						):






							"""simple docstring"""
							snake_case   :					Dict    						=				ASTFeatureExtractionTester(self						)

			def      lowerCAmelCase(   self   :  int						):






							"""simple docstring"""
							snake_case   :					Union[str, Any]    						=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
							# create three inputs of length 800, 1000, and 1200
							snake_case   :					Any    						=				[floats_list((1, x)						)[0] for x in range(800     , 1_400     , 200						)]
							snake_case   :					Dict    						=				[np.asarray(UpperCAmelCase__						) for speech_input in speech_inputs]

							# Test not batched input
							snake_case   :					List[str]    						=				feat_extract(speech_inputs[0]     , return_tensors='''np'''						).input_values
							snake_case   :					int    						=				feat_extract(np_speech_inputs[0]     , return_tensors='''np'''						).input_values
							self.assertTrue(np.allclose(UpperCAmelCase__     , UpperCAmelCase__     , atol=1e-3						)						)

							# Test batched
							snake_case   :					int    						=				feat_extract(UpperCAmelCase__     , padding=UpperCAmelCase__     , return_tensors='''np'''						).input_values
							snake_case   :					str    						=				feat_extract(UpperCAmelCase__     , padding=UpperCAmelCase__     , return_tensors='''np'''						).input_values
							for enc_seq_a, enc_seq_a in zip(UpperCAmelCase__     , UpperCAmelCase__						):
											self.assertTrue(np.allclose(UpperCAmelCase__     , UpperCAmelCase__     , atol=1e-3						)						)

							# Test 2-D numpy arrays are batched.
							snake_case   :					List[Any]    						=				[floats_list((1, x)						)[0] for x in (800, 800, 800)]
							snake_case   :					int    						=				np.asarray(UpperCAmelCase__						)
							snake_case   :					List[str]    						=				feat_extract(UpperCAmelCase__     , return_tensors='''np'''						).input_values
							snake_case   :					Union[str, Any]    						=				feat_extract(UpperCAmelCase__     , return_tensors='''np'''						).input_values
							for enc_seq_a, enc_seq_a in zip(UpperCAmelCase__     , UpperCAmelCase__						):
											self.assertTrue(np.allclose(UpperCAmelCase__     , UpperCAmelCase__     , atol=1e-3						)						)

			@require_torch
			def      lowerCAmelCase(   self   :  Optional[Any]						):






							"""simple docstring"""
							import torch

							snake_case   :					Union[str, Any]    						=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
							snake_case   :					Optional[int]    						=				np.random.rand(100						).astype(np.floataa						)
							snake_case   :					Union[str, Any]    						=				np_speech_inputs.tolist()

							for inputs in [py_speech_inputs, np_speech_inputs]:
											snake_case   :					Any    						=				feature_extractor.pad([{'''input_values''': inputs}]     , return_tensors='''np'''						)
											self.assertTrue(np_processed.input_values.dtype == np.floataa						)
											snake_case   :					Any    						=				feature_extractor.pad([{'''input_values''': inputs}]     , return_tensors='''pt'''						)
											self.assertTrue(pt_processed.input_values.dtype == torch.floataa						)

			def      lowerCAmelCase(   self   :  str     , UpperCAmelCase__   :  List[Any]						):






							"""simple docstring"""
							from datasets import load_dataset

							snake_case   :					Optional[int]    						=				load_dataset('''hf-internal-testing/librispeech_asr_dummy'''     , '''clean'''     , split='''validation'''						)
							# automatic decoding with librispeech
							snake_case   :					Union[str, Any]    						=				ds.sort('''id'''						).select(range(UpperCAmelCase__						)						)[:num_samples]['''audio''']

							return [x["array"] for x in speech_samples]



			@require_torch
			def      lowerCAmelCase(   self   :  int						):






							"""simple docstring"""
							snake_case   :					int    						=				torch.tensor(
							    [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
							     -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
							     -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
							     -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]						)
							# fmt: on

							snake_case   :					List[str]    						=				self._load_datasamples(1						)
							snake_case   :					Optional[int]    						=				ASTFeatureExtractor()
							snake_case   :					Dict    						=				feature_extractor(UpperCAmelCase__     , return_tensors='''pt'''						).input_values
							self.assertEquals(input_values.shape     , (1, 1_024, 128)						)
							self.assertTrue(torch.allclose(input_values[0, 0, :30]     , UpperCAmelCase__     , atol=1e-4						)						)






'''simple docstring'''







from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


lowerCAmelCase_     						=				logging.get_logger(__name__)  # pylint: disable=invalid-name







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   if not path:
      return "pipe"

   for ext in PipelineDataFormat.SUPPORTED_FORMATS:
      if path.endswith(UpperCAmelCase						):
         return ext

   raise Exception(
       F"""Unable to determine file format from file extension {path}. """
       F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     pipeline(
       task=args.task      ,model=args.model if args.model else None      ,config=args.config      ,tokenizer=args.tokenizer      ,device=args.device      ,)
   A__       =     try_infer_format_from_ext(args.input						) if args.format == 'infer' else args.format
   A__       =     PipelineDataFormat.from_str(
       format=UpperCAmelCase      ,output_path=args.output      ,input_path=args.input      ,column=args.column if args.column else nlp.default_input_names      ,overwrite=args.overwrite      ,)
   return RunCommand(UpperCAmelCase      ,UpperCAmelCase						)







class 	_snake_case(			UpperCAmelCase      ):






 def __init__(self       :  Tuple    ,		a       :  Pipeline    ,		a       :  PipelineDataFormat    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     nlp
    A__       =     reader






 @staticmethod
 def 						_UpperCamelCase (a       :  ArgumentParser    )       ->   str:






    """simple docstring"""


    A__       =     parser.add_parser('run'    ,		help='Run a pipeline through the CLI'    )
    run_parser.add_argument('--task'    ,		choices=get_supported_tasks()    ,		help='Task to run'    )
    run_parser.add_argument('--input'    ,		type=a    ,		help='Path to the file to use for inference'    )
    run_parser.add_argument('--output'    ,		type=a    ,		help='Path to the file that will be used post to write results.'    )
    run_parser.add_argument('--model'    ,		type=a    ,		help='Name or path to the model to instantiate.'    )
    run_parser.add_argument('--config'    ,		type=a    ,		help='Name or path to the model\'s config to instantiate.'    )
    run_parser.add_argument(
        '--tokenizer'    ,		type=a    ,		help='Name of the tokenizer to use. (default: same as the model name)'    )
    run_parser.add_argument(
        '--column'    ,		type=a    ,		help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'    ,		)
    run_parser.add_argument(
        '--format'    ,		type=a    ,		default='infer'    ,		choices=PipelineDataFormat.SUPPORTED_FORMATS    ,		help='Input format to read from'    ,		)
    run_parser.add_argument(
        '--device'    ,		type=a    ,		default=-1    ,		help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'    ,		)
    run_parser.add_argument('--overwrite'    ,		action='store_true'    ,		help='Allow overwriting the output file.'    )
    run_parser.set_defaults(func=a    )






 def 						_UpperCamelCase (self       :  int    )       ->   Optional[Any]:






    """simple docstring"""


    A__  ,			A__       =     self._nlp, []

    for entry in self._reader:
       A__       =     nlp(**a    ) if self._reader.is_multi_columns else nlp(a    )
       if isinstance(a    ,		a    ):
          outputs.append(a    )
       else:
          outputs += output

        # Saving data
    if self._nlp.binary_output:
       A__       =     self._reader.save_binary(a    )
       logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""    )
    else:
       self._reader.save(a    )


